Abstract
Depth-based 3D hand trackers are expected to estimate highly accurate poses of the human hand given the image. One of the critical problems in tracking the hand pose is the generation of realistic predictions. This paper proposes a novel “anatomical filter” that accepts a hand pose from a hand tracker and generates the closest possible pose within the real human hand’s anatomical bounds. The filter works by calculating the 26-DoF vector representing the joint angles and correcting those angles based on the real human hand’s biomechanical limitations. The proposed filter can be plugged into any hand tracker to enhance its performance. The filter has been tested on two state-of-the-art 3D hand trackers. The empirical observations show that our proposed filter improves the hand pose’s anatomical correctness and allows a smooth trade-off with pose error. The filter achieves the lowest prediction error when used with state-of-the-art trackers at 10% correction.
Highlights
Depth based 3D hand tracking is the problem of predicting the 3D hand pose given a single depth image of the hand at any angle
This paper proposes a novel “anatomical filter” that accepts a hand pose from a hand tracker and generates the closest possible pose within the real human hand’s anatomical bounds
One of the critical problems in hand tracking is the realism of the output. This problem of hand pose realism has been studied in a partial aspect as “highly accurate tracking” in earlier work as increasing the tracker’s accuracy and reducing the poses’ overall position-based error
Summary
Depth based 3D hand tracking (or hand pose estimation) is the problem of predicting the 3D hand pose given a single depth image of the hand at any angle. One of the critical problems in hand tracking is the realism of the output This problem of hand pose realism has been studied in a partial aspect as “highly accurate tracking” in earlier work as increasing the tracker’s accuracy and reducing the poses’ overall position-based error. From a human perspective (Pelphrey et al, 2005), the error can affect the internal human system leading to false information and mismatch in the motor cortex and the visual system Other solutions to this problem include inverse kinematics based solutions such as Wang and Popovic (2009) and using kinematic priors such as Thayananthan et al (2003). We elaborate on the filter rules and bounds in Anatomical Filter
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